). While a framework for optimizing the AUC loss for unstructured models exists, it does not naturally extend to structured mod-els. In this work, we propose a representa-tion and learning formulation for optimizing structured models over the AUC loss, show how our approach generalizes the unstruc

Abstract: AUC-SVM directly maximizes the area under the ROC curve (AUC) through minimizing its hinge loss relaxation, and the decision function is determined by those support vector sample pairs playing the same roles as the support vector samples in SVM. Such a learning paradigm generally

-gate losses. In this paper, we introduce the general-ized calibration for AUC optimization, and prove that it is a necessary condition for AUC consis-tency. We then provide a sufficient condition for AUC consistency, and show its usefulness in study-ing the consistency of various surrogate losses, as well

in his metapsychological and clinical thinking, so much so that contemporary historians of the psychoanalytic movement generally agree that Freud's explorations into narcissism were central to the development of his (a) structural model (id, ego, and superego); (b) concept of the ego ideal

intentions to drop out of high school, which are later implemented, leading to actual dropout behavior. This model was tested with high school students (N = 4,537) by means of a prospective design. Results from analyses of variance and a structural equation modeling analysis (with L1SREL) were found

-based dissimilarity coefficient. Our approach generalizes latent svm in two important ways: (i) it models the uncertainty over latent variables instead of relying on a pointwise estimate; and (ii) it allows the use of loss functions that depend on latent variables, which greatly increases its applicability. We

import would be felt on a micro-scale. Indeed, decision-rules and public perceptions could take on forms that would be quite particular to the set of available options. Taken in its most general form, the vulnerability model reflected in equations (1) and (2) can lead to the conclusion that everything

on a correct representation of the target concept. The underlying model is called identification in the limit. The present study concerns different versions of incremental learning in the limit. In contrast to the general case, now the learner has only limited access to the examples provided so far

Inductive learning searches an optimal hypothesis that minimizes a given loss function. It is usually assumed that the simplest hypothesis that fits the data is the best approximate to an optimal hypothesis. Since finding the simplest hypothesis is NP-hard for most representations, we generally

Within the statistical learning framework, this paper studies the regression model associ-ated with the correntropy induced losses. The correntropy, as a similarity measure, has been frequently employed in signal processing and pattern recognition. Motivated by its empirical successes, this paper